Towards an Industry 4.0-Based Maintenance Approach in the Manufacturing Processes

Garcia, José Roberto Reyes; Martinetti, Alberto; Becker, Juan Manuel Jauregui; Singh, Sarbjeet; Dongen, Leonardus Adriana Maria van

doi:10.4018/978-1-7998-8548-1.ch062

Cited by 3 publications

(5 citation statements)

References 35 publications

(3 reference statements)

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“…In [1], the main maintenance areas supported and enabled by Industry 4.0 technologies were discussed, along with the introduction of an Industry 4.0-based predictive maintenance approach for the manufacturing industry. In [2], various Industry 4.0 enabling technologies that enhance operational flexibility in final assembly were presented, with proven examples of their application in increasing flexibility.…”

Section: Related Workmentioning

confidence: 99%

“…One of the significant applications of Industry 4.0 in maintenance is the use of AR to enhance the maintenance approach. AR is a technology that overlays virtual objects onto the real world, enabling operators to visualize and interact with virtual objects in real-time [1] (see Figure 1). The benefits of an AR-based maintenance system are numerous.…”

Section: Introductionmentioning

confidence: 99%

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Augmented Reality Based Distant Maintenance Approach

et al. 2023

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This paper focuses on the implementation of Industry 4.0-based maintenance approaches using AR technology. The primary objective is to highlight the benefits of AR in maintenance and provide a framework for implementing AR-based maintenance systems. AR can assist maintenance personnel in performing maintenance tasks more efficiently by providing real-time instructions and information. By overlaying virtual information on real equipment, AR can guide maintenance personnel through maintenance procedures, reducing human errors and improving the quality of the work. AR can also provide remote assistance to maintenance personnel, allowing experts to remotely guide less experienced personnel through complex maintenance procedures. The proposed application provides motorcycle service assistance using AR and telepresence technologies. It enables remote monitoring of the service flow and performs an automated identification of parts. The system provides instructions for disassembling more complex mechanical components, provides a 3D model of the object, and enables ordering of the necessary parts.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Augmented Reality Based Distant Maintenance Approach

et al. 2023

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“…Solidity, area, and bounding box are the features used for classification. In [2], a hybrid kernel-based support vector machine (HKSVM) was designed to identify brain tumors from MRI. At first, anisotropic filters are applied to images to isolate them from noise, and morphological operators are used to complete image segmentation.…”

Section: Related Workmentioning

confidence: 99%

“…Reliable and efficient collection and communication of observations from physical information systems that support the Internet of Things (IoT), for example sensors laid at faraway locations, to dominate centers are the current issues facing data detection in big data areas [1][2][3]. With the heavy use of devices in the healthcare trade, low-power communication equipment, and restricted reliability availability, there will be a variety of security threats.…”

Section: Introductionmentioning

confidence: 99%

Computational Analysis based on Advanced Correlation Automatic Detection Technology in BDD-FFS System

Zheng¹,

Tahir²,

Li³

et al. 2022

IJACSA

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Big Data-Driven Fabric Future Systems (BDD-FFS) is currently attracting widespread attention in the healthcare research community. Medical devices rely primarily on the intelligent Internet to gather important health-related information. According to this, we provide patients with deeply supportive data to help them through their recovery. However, due to the large number of medical devices, the address of the device can be modified by intruders, which can be life-threatening for serious patients (such as tumor patients). A large number of abnormal cells in the brain can lead to brain tumors, which harm brain tissue and can be life-threatening. Recognition of brain tumors at the beginning of the process is significant for their detection, prediction and therapy. The traditional approach for detecting is for a human to perform a biopsy and examine CT scans or magnetic resonance imaging (MRI), which is cumbersome,unrealistic for great amounts of resource, and requests the radiologist to make inferential computations. A variety of automation schemes have been designed to address these challenges. However, there is an urgent need to develop a technology that will detect brain tumors with remarkable accuracy in a much shorter time. In addition, the selection of feature sets for prediction is crucial to realize significant accuracy. This work utilizes an associative action learner with an advanced feature group, Partial Tree (PART-T), to detect brain tumor recognition grades. The model presented was compared with existing methods through 10-fold cross-validation. Experimental results show that partial trees with advanced feature sets are superior to existing techniques in terms of performance indicators used for evaluation, such as accuracy, recall rate and F-measure.

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“…The prescriptive strategy is the development/improvement of the predictive approach, in which the system (by collecting data in real time) calculates the estimated time of operation of the device until a failure occurs. In the prescriptive concept, it is possible to obtain information on how to avoid failure and what its consequences may be [18]. It also uses: Internet of Things, Machine Learning, Big Data analysis methods [19] and Artificial Intelligence methods [20].…”

Section: Introductionmentioning

confidence: 99%

A Strategy for Managing the Operation of Technical Infrastructure Based on the Analysis of “Bad Actors”—A Case Study of LOTOS Group S.A.

2022

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The article covers issues related to the selection of a management strategy for the operation of technical infrastructure. It contains the approaches, methodology and a general model of the system of operation of the Maintenance Services of the LOTOS Group production company, which is an example of a large oil concern. Nowadays, an increasing aspect is put on the efficiency of individual repair processes, as well as the operation of equipment. Doubts often arise as to which strategy is the right one and which one to choose. With numerous machines, there are problems related to work planning and optimal use of resources. In addition, there are frequently difficulties in identifying the devices that interfere with the processes. The aim of this article is to present the maintenance strategy of the LOTOS production company and to introduce a procedure that allows for the assessment of the efficiency of machinery and equipment operation. It includes a method extending the identification of “Bad Actors” used so far, based on data from the ERP (Enterprise Resource Planning) system, with a solution based on reports made after device repairs. MTBR (Mean Time Between Repair) coefficients were calculated for particular Complexes, Plants and Machines, which allowed for the identification of troublesome areas and the most difficult devices in production processes. This made it possible to select the equipment that should be repaired in the first place. After the implementation of the method and service activities, a decrease in the number of problematic machines was observed.

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Towards an Industry 4.0-Based Maintenance Approach in the Manufacturing Processes

Cited by 3 publications

References 35 publications

Augmented Reality Based Distant Maintenance Approach

Augmented Reality Based Distant Maintenance Approach

Computational Analysis based on Advanced Correlation Automatic Detection Technology in BDD-FFS System

A Strategy for Managing the Operation of Technical Infrastructure Based on the Analysis of “Bad Actors”—A Case Study of LOTOS Group S.A.

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